Awesome-BFM-Papers

May 26, 2026 · View on GitHub

Awesome Paper

A curated list of behavior(al) foundation model (BFM) papers, articles, tutorials, slides, and projects.

Table of Contents

What is the Behavior Foundation Model?



A behavior foundation model learns broad behavior priors from large-scale and diverse behavior data, which can then be conveniently adapted to a wide range of downstream tasks.

Paper List

An overview of the pre-training pipelines and adaptation strategies for BFMs discussed in this review. The goal-conditioned learning requires an extrinsic reward function and large-scale human data, while intrinsic reward-driven learning uses intrinsic rewards generated by self-supervised tasks. In contrast, the forward-backward representation learning learns a forward embedding network (FEN) and a backward embedding network (BEN) using reward-free transitions, which can then be combined with a specific reward function to infer a policy. For adaptation strategies, BFMs can be fine-tuned through common approaches, such as full fine-tuning (FFT) and low-rank adaptation (LoRA), as well as methods like latent space adaptation, which adjust the policy by modifying the latent task vector. Beyond fine-tuning, adaptation also involves strategies for hierarchical control, where high-level planners (\textit{e.g.}, generative models like LLMs or diffusion models) process abstract goals and generate subtasks for the BFM to execute as a low-level controller, enabling complex and long-horizon task completion.



Pre-training

Forward-Backward Representation Learning

📅Year🗃️Archive🔤Title📜Paper🖥️Code
2025arXivBFM-Zero: Promptable Behavioral Foundation Model for Humanoid Control Using Unsupervised Reinforcement LearningPaperCode
2025ICLRZero-shot Whole-body Humanoid Control via Behavioral Foundation ModelsPaperCode
2024arXivFiner Behavioral Foundation Models via Auto-regressive Features and Advantage WeightingPaperN/A
2024NeurIPSFast Imitation via Behavior Foundation ModelsPaperN/A
2021NeurIPSLearning One Representation to Optimize All RewardsPaperCode
2021arXivLearning Successor States and Goal-Dependent Values: A Mathematical ViewpointPaperN/A

Goal-Conditioned Learning

📅Year🗃️Archive🔤Title📜Paper🖥️Code
2025arXivSonic: Supersizing Motion Tracking for Natural Humanoid Whole-Body ControlPaperCode
2025arXivTrack Any Motions under Any DisturbancesPaperCode
2025arXivAgility Meets Stability: Versatile Humanoid Control with Heterogeneous DataPaperCode
2025arXivTWIST2: Scalable, Portable, and Holistic Humanoid Data Collection SystemPaperCode
2025CoRLTWIST: Teleoperated Whole-Body Imitation SystemPaperCode
2025CoRLCLONE: Closed-Loop Whole-Body Humanoid Teleoperation for Long-Horizon TasksPaperCode
2025arXivBehavior Foundation Model for Humanoid RobotsPaperCode
2025ICRAHOVER: Versatile Neural Whole-Body Controller for Humanoid RobotsPaperCode
2025CVPRInterMimic: Towards Universal Whole-Body Control for Physics-Based Human-Object InteractionsPaperCode
2025arXivModSkill: Physical Character Skill ModularizationPaperCode
2024TOGMaskedMimic: Unified Physics-Based Character Control Through Masked Motion InpaintingPaperCode
2024ICLRH-GAP: Humanoid Control with a Generalist PlannerPaperCode
2024SIGGRAPHCALM: Conditional Adversarial Latent Models for Directable Virtual CharactersPaperCode
2023TOGMoConVQ: Unified Physics-Based Motion Control via Scalable Discrete RepresentationsPaperN/A
2023SIGGRAPH AsiaCASE: Learning Conditional Adversarial Skill Embeddings for Physics-Based CharactersPaperCode
2023ICCVPHC: Perpetual Humanoid Control for Real-Time Simulated AvatarsPaperCode
2021Science RoboticsTeamPlay: From Motor Control to Team Play in Simulated Humanoid FootballPaperN/A
2023ICMLMTM: Masked Trajectory Models for Prediction, Representation, and ControlPaperCode
2022TOGASE: Large-scale Reusable Adversarial Skill Embeddings for Physically Simulated CharactersPaperCode

Intrinsic Reward-Driven Learning

📅Year🗃️Archive🔤Title📜Paper🖥️Code
2021ICMLActive Pretraining with Successor FeaturesPaperCode
2021ICMLReinforcement Learning with Prototypical RepresentationsPaperCode
2020ICMLState Entropy Maximization with Random Encoders for Efficient ExplorationPaperCode
2019ICLRExploration by Random Network DistillationPaperCode
2018ICLRDiversity is All You Need: Learning Skills without a Reward FunctionPaperCode

Adaptation

Fine-tuning Techniques

📅Year🗃️Archive🔤Title📜Paper🖥️Code
2026arXivAny2Any: Efficient Cross-Embodiment Transfer for Humanoid Whole-Body TrackingPaperN/A
2025arXivTask Tokens: A Flexible Approach to Adapting Behavior Foundation ModelsPaperN/A
2025arXivZero-Shot Adaptation of Behavioral Foundation Models to Unseen DynamicsPaperN/A
2025CoRLFast Adaptation With Behavioral Foundation ModelsPaperN/A

Towards Hierarchical Control

📅Year🗃️Archive🔤Title📜Paper🖥️Code
2025arXivSENTINEL: A Fully End-to-End Language-Action Model for Humanoid Whole Body ControlPaperN/A
2025arXivBeyondMimic: From Motion Tracking to Versatile Humanoid Control via Guided DiffusionPaperCode
2025arXivLeVerb: Humanoid Whole-Cody Control with Latent Vision-Language InstructionPaperN/A
2025arXivLangWBC: Language-Directed Humanoid Whole-Body Control via End-to-end LearningPaperN/A
2025CVPRTokenhsi: Unified Synthesis of Physical Human-Scene Interactions through Task TokenizationPaperCode
2024ICLRCloSD: Closing the Loop between Simulation and Diffusion for Multi-task Character ControlPaperCode
2024arXivUniPhys: Unified Planner and Controller with Diffusion for Flexible Physics-based Character ControlPaperCode
2023ICLRUnified Human-Scene Interaction via Prompted Chain-of-ContactsPaperCode

Datasets

📅Year🗃️Archive💽Dataset🎞️Clip⌚Hour📜Paper🖥️Code
2025HumanoidsHumanoid-X163800240.0PaperCode
2025arXivPHUMA7600073.0PaperCode
2025arXivMotion-X++120462180.9PaperCode
2023NIPSMotion-X81084144.2PaperCode
2022ECCVPoseScript--PaperCode
2022CVPRHumanML3D1461628.6PaperCode
2021CVPRBABEL1322043.5PaperCode
2020TOGLAFAN77.04.6PaperCode
2019ICCVAMASS1126540.0PaperCode
2016arXivKIT-ML391111.2PaperCode

Cite Us

If this project helped your work, please cite us by

@article{yuan2025bfm,
    title={A Survey of Behavior Foundation Model: Next-Generation Whole-Body Control System of Humanoid Robots},
    author={Yuan, Mingqi and Yu, Tao and Ge, Wenqi and Yao, Xiuyong and Li, Dapeng and Wang, Huijiang and Chen, Jiayu and Li, Bo and Zhang, Wei and Zeng, Wenjun and Chen, Hua and Jin, Xin},
    journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
    year={2025},
    publisher={IEEE}
}